Behavioral Barriers and the Socioeconomic Gap in Child Care Enrollment
Henning Hermes, Philipp Lergetporer, Frauke Peter, Simon Wiederhold
Abstract
Children with lower socioeconomic status (SES) tend to benefit more from early child care, but are substantially less likely to be enrolled. We study whether reducing behavioral barriers in the application process increases enrollment in child care for lower-SES children. In our RCT in Germany with highly subsidized child care (n > 600), treated families receive application information and personal assistance for applications. For lower-SES families, the treatment increases child care application rates by 21 pp and enrollment rates by 16 pp. Higher-SES families are not affected by the treatment. Thus, alleviating behavioral barriers closes half of the SES gap in early child care enrollment.
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Conditional Macroeconomic Forecasts: Disagreement, Revisions and Forecast Errors
Alexander Glas, Katja Heinisch
IWH Discussion Papers,
Nr. 7,
2021
Abstract
Using data from the European Central Bank‘s Survey of Professional Forecasters, we analyse the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the heterogeneity, updating and ex-post performance of predictions for inflation, real GDP growth and the unemployment rate are related to assumptions about future oil prices, exchange rates, interest rates and wage growth. Our findings indicate that inflation forecasts are closely associated with oil price expectations, whereas expected interest rates are used primarily to predict output growth and unemployment. Expectations about exchange rates and wage growth also matter for macroeconomic forecasts, albeit less so than oil prices and interest rates. We show that survey participants can considerably improve forecast accuracy for macroeconomic outcomes by reducing prediction errors for external conditions. Our results contribute to a better understanding of the expectation formation process of experts.
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Productivity and Employment in APAC Economies: A Comparison With the EU Using Firm-Level Information
Hoang Minh Duy, Filippo di Mauro, Peter Morgan
ADBI Working Paper,
Nr. 1264,
2021
Abstract
We provide an overview of productivity development and other related indicators in Asia and Pacific (APAC) countries, with comparisons with the Europe region. We use the seventh vintage firm-level data from the Productivity Research Network in the APAC region and CompNet in Europe for our study. The overall results show that the productivity growth in developed APAC countries (Australia, New Zealand, and the Republic of Korea) is significantly ahead of the growth in developing APAC countries (India and the People’s Republic of China) and on par with the EU’s growth. There is an ongoing process of bottom firms catching up with top firms in the Republic of Korea and the richest EU countries. Regarding employment and labor skills, employment growth has generally been quite stagnant in all regions. Labor skills, for which we use the wage premium as a proxy, are quite similar across most regions, with the richest EU countries showing a higher premium than the rest. Our test of the productivity–employment link indicates that the size of employment tends to have a greater impact on productivity in APAC countries, while labor skills have greater emphasis in the EU.
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Introduction to "Measuring and Accounting for Innovation in the Twenty-First Century"
Javier Miranda
Measuring and Accounting for Innovation in the Twenty-First Century,
NBER Studies in Income and Wealth, Vol 78 /
2021
Abstract
Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies. This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution. The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.
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Measuring and Accounting for Innovation in the Twenty-First Century
Carol Corrado, Jonathan Haskel, Javier Miranda, Daniel Sichel
NBER Studies in Income and Wealth,
2021
Abstract
Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies.
This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution.
The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.
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On the International Dissemination of Technology News Shocks
João Carlos Claudio, Gregor von Schweinitz
IWH Discussion Papers,
Nr. 25,
2020
Abstract
This paper investigates the propagation of technology news shocks within and across industrialised economies. We construct quarterly utilisation-adjusted total factor productivity (TFP) for thirteen OECD countries. Based on country-specific structural vector autoregressions (VARs), we document that (i) the identified technology news shocks induce a quite homogeneous response pattern of key macroeconomic variables in each country; and (ii) the identified technology news shock processes display a significant degree of correlation across several countries. Contrary to conventional wisdom, we find that the US are only one of many different sources of technological innovations diffusing across advanced economies. Technology news propagate through the endogenous reaction of monetary policy and via trade-related variables. That is, our results imply that financial markets and trade are key channels for the dissemination of technology.
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What’s slowing down the European Banking Union?
Simon Grothe, Michael Koetter, Thomas Krause, Lena Tonzer
LSE Business Review,
2020
Abstract
Differences in national bank regulation and supervision hamper the process; political factors play a minor role, write Simon Grothe, Michael Koetter, Thomas Krause, and Lena Tonzer
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Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Pacific-Basin Finance Journal,
June
2020
Abstract
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
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Does Low-pay Persist across Different Regimes? Evidence from the German Unification
André Diegmann, Nicole Gürtzgen
Economics of Transition and Institutional Change,
Nr. 3,
2020
Abstract
Using German administrative data, we study across-regime low-pay persistence in the context of an economic transformation process. We first show that individuals' initial allocation to the post-unification low-wage sector was close to random in terms of market-regime unobservables. Consistent with a weak connection between individuals' true productivity and their pre-unification low-wage status, the extent of across-regime state dependence is found to be small and appears to vanish over time. For males, across-regime state dependence is most pronounced among the medium- and high-skilled, suggesting the depreciation of human capital as an explanation.
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How to Talk Down Your Stock Performance
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking, Severin Zörgiebel
SSRN Discussion Papers,
2020
Abstract
We process the natural language of verbal firm disclosures in order to study the use of context specific language or jargon and its impact on financial performance. We observe that, within the Q&A of earnings conference calls, managers use less jargon in responses to tougher questions, and after a quarter of bad economic success. Moreover, markets interpret the lack of precise information as a bad signal: we find lower cumulative abnormal returns and a higher implied volatility following earnings calls where managers use less jargon. These results support the argument that context specific language or jargon helps to efficiently and precisely transfer information.
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